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module OpenTox
module Model
include OpenTox
def run(params)
if CONFIG[:yaml_hosts].include?(URI.parse(@uri).host)
accept = 'application/x-yaml'
else
accept = 'application/rdf+xml'
end
begin
params[:acccept] = accept
#TODO fix: REstClientWrapper does not accept accept header
#RestClientWrapper.post(@uri,params)#,{:accept => accept})
`curl -X POST -H "Accept:#{accept}" #{params.collect{|k,v| "-d #{k}=#{v}"}.join(" ")} #{@uri}`.to_s.chomp
rescue => e
LOGGER.error "Failed to run #{@uri} with #{params.inspect} (#{e.inspect})"
raise "Failed to run #{@uri} with #{params.inspect}"
end
end
=begin
def classification?
#TODO replace with request to ontology server
if @metadata[DC.title] =~ /(?i)classification/
return true
elsif @metadata[DC.title] =~ /(?i)regression/
return false
elsif @uri =~/ntua/ and @metadata[DC.title] =~ /mlr/
return false
elsif @uri =~/tu-muenchen/ and @metadata[DC.title] =~ /regression|M5P|GaussP/
return false
elsif @uri =~/ambit2/ and @metadata[DC.title] =~ /pKa/ || @metadata[DC.title] =~ /Regression|Caco/
return false
elsif @uri =~/majority/
return (@uri =~ /class/) != nil
else
raise "unknown model, uri:'"+@uri+"' title:'"+@metadata[DC.title]+"'"
end
end
=end
class Generic
include Model
end
class Lazar
include Model
#attr_accessor :prediction_type, :feature_type, :features, :effects, :activities, :p_values, :fingerprints, :parameters
attr_accessor :compound, :prediction_dataset, :features, :effects, :activities, :p_values, :fingerprints, :parameters, :feature_calculation_algorithm, :similarity_algorithm, :prediction_algorithm
def initialize(uri=nil)
if uri
super uri
else
super CONFIG[:services]["opentox-model"]
end
# TODO: fix metadata, add parameters
@metadata[OT.algorithm] = File.join(CONFIG[:services]["opentox-algorithm"],"lazar")
@features = []
@effects = {}
@activities = {}
@p_values = {}
@fingerprints = {}
@feature_calculation_algorithm = "substructure_match"
@similarity_algorithm = "weighted_tanimoto"
@prediction_algorithm = "weighted_majority_vote"
@min_sim = 0.3
end
def self.find(uri)
YAML.load RestClientWrapper.get(uri,:content_type => 'application/x-yaml')
end
def self.create_from_dataset(dataset_uri,feature_dataset_uri,prediction_feature=nil)
training_activities = OpenTox::Dataset.find(dataset_uri)
training_features = OpenTox::Dataset.find(feature_dataset_uri)
unless prediction_feature # try to read prediction_feature from dataset
raise "#{training_activities.features.size} features in dataset #{dataset_uri}. Please provide a prediction_feature parameter." unless training_activities.features.size == 1
prediction_feature = training_activities.features.keys.first
params[:prediction_feature] = prediction_feature
end
lazar = Lazar.new
training_features = OpenTox::Dataset.new(feature_dataset_uri)
case training_features.feature_type
when "classification"
lazar.similarity_algorithm = "weighted_tanimoto"
when "regression"
lazar.similarity_algorithm = "weighted_euclid"
end
end
def self.create(dataset_uri,prediction_feature=nil,feature_generation_uri=File.join(CONFIG[:services]["opentox-algorithm"],"fminer/bbrc"),params=nil)
training_activities = OpenTox::Dataset.find(dataset_uri)
unless prediction_feature # try to read prediction_feature from dataset
raise "#{training_activities.features.size} features in dataset #{dataset_uri}. Please provide a prediction_feature parameter." unless training_activities.features.size == 1
prediction_feature = training_activities.features.keys.first
params[:prediction_feature] = prediction_feature
end
lazar = Lazar.new
params[:feature_generation_uri] = feature_generation_uri
feature_dataset_uri = OpenTox::Algorithm::Generic.new(feature_generation_uri).run(params).to_s
training_features = OpenTox::Dataset.find(feature_dataset_uri)
raise "Dataset #{feature_dataset_uri} not found or empty." if training_features.nil?
# sorted features for index lookups
lazar.features = training_features.features.sort if training_features.feature_type == "regression"
training_features.data_entries.each do |compound,entry|
lazar.fingerprints[compound] = [] unless lazar.fingerprints[compound]
entry.keys.each do |feature|
case training_features.feature_type
when "fminer"
# fingerprints are sets
smarts = training_features.features[feature][OT.smarts]
lazar.fingerprints[compound] << smarts
unless lazar.features.include? smarts
lazar.features << smarts
lazar.p_values[smarts] = training_features.features[feature][OT.p_value]
lazar.effects[smarts] = training_features.features[feature][OT.effect]
end
when "classification"
# fingerprints are sets
if entry[feature].flatten.size == 1
lazar.fingerprints[compound] << feature if entry[feature].flatten.first.match(TRUE_REGEXP)
lazar.features << feature unless lazar.features.include? feature
else
LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{compound}, feature #{feature}"
end
when "regression"
# fingerprints are arrays
if entry[feature].flatten.size == 1
lazar.fingerprints[compound][lazar.features.index(feature)] = entry[feature].flatten.first
else
LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{compound}, feature #{feature}"
end
end
end
lazar.activities[compound] = [] unless lazar.activities[compound]
training_activities.data_entries[compound][params[:prediction_feature]].each do |value|
case value.to_s
when "true"
lazar.activities[compound] << true
when "false"
lazar.activities[compound] << false
else
lazar.activities[compound] << value.to_f
lazar.prediction_type = "regression"
end
end
end
if feature_generation_uri.match(/fminer/)
lazar.feature_calculation_algorithm = "substructure_match"
else
halt 404, "External feature generation services not yet supported"
end
lazar.metadata[OT.dependentVariables] = params[:prediction_feature]
lazar.metadata[OT.trainingDataset] = dataset_uri
lazar.metadata[OT.featureDataset] = feature_dataset_uri
lazar.parameters = {
"dataset_uri" => dataset_uri,
"prediction_feature" => prediction_feature,
"feature_generation_uri" => feature_generation_uri
}
model_uri = lazar.save
LOGGER.info model_uri + " created #{Time.now}"
model_uri
end
def predict_dataset(dataset_uri)
@prediction_dataset = Dataset.create
@prediction_dataset.add_metadata({
OT.hasSource => @lazar.uri,
DC.creator => @lazar.uri,
DC.title => URI.decode(File.basename( @metadata[OT.dependentVariables] ))
})
@prediction_dataset.add_parameters({"dataset_uri" => dataset_uri})
Dataset.new(dataset_uri).load_compounds.each do |compound_uri|
predict(compound_uri,false)
end
@prediction_dataset.save
@prediction_dataset.uri
end
def predict(compound_uri,verbose=false)
@compound = Compound.new compound_uri
unless @prediction_dataset
@prediction_dataset = Dataset.create
@prediction_dataset.add_metadata( {
OT.hasSource => @lazar.uri,
DC.creator => @lazar.uri,
DC.title => URI.decode(File.basename( @metadata[OT.dependentVariables] ))
} )
@prediction_dataset.add_parameters( {"compound_uri" => compound_uri} )
end
neighbors
eval @prediction_algorithm
if @prediction
feature_uri = File.join( @prediction_dataset.uri, "feature", @prediction_dataset.compounds.size)
@prediction_dataset.add @compound.uri, feature_uri, @prediction
feature_metadata = @prediction_dataset.metadata
feature_metadata[DC.title] = File.basename(@metadata[OT.dependentVariables])
feature_metadata[OT.prediction] = @prediction
feature_metadata[OT.confidence] = @confidence
@prediction_dataset.add_feature(feature_uri, feature_metadata)
if verbose
if @compound_features
@compound_features.each do |feature|
@prediction_dataset.add @compound.uri, feature, true
end
end
n = 0
@neighbors.sort{|a,b| a[:similarity] <=> b[:similarity]}.each do |neighbor|
neighbor_uri = File.join( @prediction_dataset.uri, "feature/neighbor", n )
@prediction_dataset.add @compound.uri, neighbor_uri, true
@prediction_dataset.add_feature(neighbor, {
OT.compound => neighbor[:compound],
OT.similarity => neighbor[:similarity],
OT.activity => neighbor[:activity]
})
n+=1
end
end
end
@prediction_dataset.save
@prediction_dataset.uri
end
def weighted_majority_vote
conf = 0.0
@neighbors.each do |neighbor|
case neighbor[:activity].to_s
when 'true'
conf += OpenTox::Algorithm.gauss(neighbor[:similarity])
when 'false'
conf -= OpenTox::Algorithm.gauss(neighbor[:similarity])
end
end
if conf > 0.0
@prediction = true
elsif conf < 0.0
@prediction = false
else
@prediction = nil
end
@confidence = conf/@neighbors.size if @neighbors.size > 0
end
def local_svm_regression
sims = @neighbors.collect{ |n| n[:similarity] } # similarity values between query and neighbors
conf = sims.inject{|sum,x| sum + x }
acts = @neighbors.collect do |n|
act = n[:activity]
# TODO: check this in model creation
raise "0 values not allowed in training dataset. log10 is calculated internally." if act.to_f == 0
Math.log10(act.to_f)
end # activities of neighbors for supervised learning
neighbor_matches = @neighbors.collect{ |n| n[:features] } # as in classification: URIs of matches
gram_matrix = [] # square matrix of similarities between neighbors; implements weighted tanimoto kernel
if neighbor_matches.size == 0
raise "No neighbors found"
else
# gram matrix
(0..(neighbor_matches.length-1)).each do |i|
gram_matrix[i] = []
# lower triangle
(0..(i-1)).each do |j|
sim = OpenTox::Algorithm.weighted_tanimoto(neighbor_matches[i], neighbor_matches[j], @lazar.p_values)
gram_matrix[i] << OpenTox::Algorithm.gauss(sim)
end
# diagonal element
gram_matrix[i][i] = 1.0
# upper triangle
((i+1)..(neighbor_matches.length-1)).each do |j|
sim = OpenTox::Algorithm.weighted_tanimoto(neighbor_matches[i], neighbor_matches[j], @lazar.p_values) # double calculation?
gram_matrix[i] << OpenTox::Algorithm.gauss(sim)
end
end
@r = RinRuby.new(false,false) # global R instance leads to Socket errors after a large number of requests
@r.eval "library('kernlab')" # this requires R package "kernlab" to be installed
LOGGER.debug "Setting R data ..."
# set data
@r.gram_matrix = gram_matrix.flatten
@r.n = neighbor_matches.size
@r.y = acts
@r.sims = sims
LOGGER.debug "Preparing R data ..."
# prepare data
@r.eval "y<-as.vector(y)"
@r.eval "gram_matrix<-as.kernelMatrix(matrix(gram_matrix,n,n))"
@r.eval "sims<-as.vector(sims)"
# model + support vectors
LOGGER.debug "Creating SVM model ..."
@r.eval "model<-ksvm(gram_matrix, y, kernel=matrix, type=\"nu-svr\", nu=0.8)"
@r.eval "sv<-as.vector(SVindex(model))"
@r.eval "sims<-sims[sv]"
@r.eval "sims<-as.kernelMatrix(matrix(sims,1))"
LOGGER.debug "Predicting ..."
@r.eval "p<-predict(model,sims)[1,1]"
@prediction = 10**(@r.p.to_f)
LOGGER.debug "Prediction is: '" + prediction.to_s + "'."
@r.quit # free R
end
@confidence = conf/@neighbors.size if @neighbors.size > 0
end
def neighbors
@compound_features = eval(@feature_calculation_algorithm) if @feature_calculation_algorithm
@neighbors = {}
@activities.each do |training_compound,activities|
@training_compound = training_compound
sim = eval(@similarity_algorithm)
if sim > @min_sim
activities.each do |act|
@neighbors << {
:compound => @training_compound,
:similarity => sim,
:features => @fingerprints[@training_compound],
:activity => act
}
end
end
end
end
def tanimoto
OpenTox::Algorithm.tanimoto(@compound_features,@fingerprints[@training_compound])
end
def weighted_tanimoto
OpenTox::Algorithm.tanimoto(@compound_features,@fingerprints[@training_compound],@p_values)
end
def euclid
OpenTox::Algorithm.tanimoto(@compound_features,@fingerprints[@training_compound])
end
def weighted_euclid
OpenTox::Algorithm.tanimoto(@compound_features,@fingerprints[@training_compound],@p_values)
end
def substructure_match
@compound.match(@features)
end
def database_search
#TODO add features method to dataset
Dataset.new(@metadata[OT.featureDataset]).features(@compound.uri)
end
def database_activity(compound_uri)
prediction = OpenTox::Dataset.new
# find database activities
if @activities[compound_uri]
@activities[compound_uri].each { |act| prediction.add compound_uri, @metadata[OT.dependentVariables], act }
prediction.add_metadata(OT.hasSource => @metadata[OT.trainingDataset])
prediction
else
nil
end
end
def save
RestClientWrapper.post(@uri,{:content_type => "application/x-yaml"},self.to_yaml)
end
def self.all
RestClientWrapper.get(CONFIG[:services]["opentox-model"]).to_s.split("\n")
end
def delete
RestClientWrapper.delete @uri unless @uri == CONFIG[:services]["opentox-model"]
end
end
end
end
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